摘要
The dynamicity of available resources and net- work conditions, such as channel capacity and traffic charac- teristics, have posed major challenges to scheduling in wire- less networks. Reinforcement learning （RL） enables wire- less nodes to observe their respective operating environment, learn, and make optimal or near-optimal scheduling deci- sions. Learning, which is the main intrinsic characteristic of RL, enables wireless nodes to adapt to most forms of dynamicity in the operating environment as time goes by. This paper presents an extensive review on the application of the traditional and enhanced RL approaches to various types of scheduling schemes, namely packet, sleep-wake and task schedulers, in wireless networks, as well as the advantages and performance enhancements brought about by RL. Addi- tionally, it presents how various challenges associated with scheduling schemes have been approached using RL. Finally, we discuss various open issues related to RL-based schedul- ing schemes in wireless networks in order to explore new re- search directions in this area. Discussions in this paper are presented in a tutorial manner in order to establish a founda- tion for further research in this field.
The dynamicity of available resources and net- work conditions, such as channel capacity and traffic charac- teristics, have posed major challenges to scheduling in wire- less networks. Reinforcement learning （RL） enables wire- less nodes to observe their respective operating environment, learn, and make optimal or near-optimal scheduling deci- sions. Learning, which is the main intrinsic characteristic of RL, enables wireless nodes to adapt to most forms of dynamicity in the operating environment as time goes by. This paper presents an extensive review on the application of the traditional and enhanced RL approaches to various types of scheduling schemes, namely packet, sleep-wake and task schedulers, in wireless networks, as well as the advantages and performance enhancements brought about by RL. Addi- tionally, it presents how various challenges associated with scheduling schemes have been approached using RL. Finally, we discuss various open issues related to RL-based schedul- ing schemes in wireless networks in order to explore new re- search directions in this area. Discussions in this paper are presented in a tutorial manner in order to establish a founda- tion for further research in this field.
